Neural Program Search: Solving Programming Tasks from Description and Examples
Illia Polosukhin, Alexander Skidanov

TL;DR
This paper introduces Neural Program Search, a method that generates programs from natural language descriptions and examples by combining deep learning with program synthesis techniques, using a specialized DSL and a guided search algorithm.
Contribution
It proposes a novel neural program search algorithm that integrates a domain-specific language and a Seq2Tree model to improve program generation from descriptions and examples.
Findings
Outperforms sequence-to-sequence models with attention baseline
Uses a semi-synthetic dataset for evaluation
Demonstrates effective program synthesis from natural language and examples
Abstract
We present a Neural Program Search, an algorithm to generate programs from natural language description and a small number of input/output examples. The algorithm combines methods from Deep Learning and Program Synthesis fields by designing rich domain-specific language (DSL) and defining efficient search algorithm guided by a Seq2Tree model on it. To evaluate the quality of the approach we also present a semi-synthetic dataset of descriptions with test examples and corresponding programs. We show that our algorithm significantly outperforms a sequence-to-sequence model with attention baseline.
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Taxonomy
TopicsSoftware Engineering Research · Topic Modeling · Natural Language Processing Techniques
